Word association norms, mutual information, and lexicography
Computational Linguistics
WordNet: a lexical database for English
Communications of the ACM
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Effects of adjective orientation and gradability on sentence subjectivity
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Opinion observer: analyzing and comparing opinions on the Web
WWW '05 Proceedings of the 14th international conference on World Wide Web
Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Topic sentiment mixture: modeling facets and opinions in weblogs
Proceedings of the 16th international conference on World Wide Web
Learning from labeled features using generalized expectation criteria
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Sentiment analysis of blogs by combining lexical knowledge with text classification
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Just how mad are you? finding strong and weak opinion clauses
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Expanding domain sentiment lexicon through double propagation
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
SemEval-2010 task 18: Disambiguating sentiment ambiguous adjectives
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
CityU-DAC: Disambiguating sentiment-ambiguous adjectives within context
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
YSC-DSAA: An approach to disambiguate sentiment ambiguous adjectives based on SAAOL
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
Disambiguating dynamic sentiment ambiguous adjectives
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Supporting Arabic cross-lingual retrieval using contextual information
IRFC'11 Proceedings of the Second international conference on Multidisciplinary information retrieval facility
Markov blankets and meta-heuristics search: sentiment extraction from unstructured texts
WebKDD'04 Proceedings of the 6th international conference on Knowledge Discovery on the Web: advances in Web Mining and Web Usage Analysis
An information theoretic approach to sentiment polarity classification
Proceedings of the 2nd Joint WICOW/AIRWeb Workshop on Web Quality
Sentic Computing: Techniques, Tools, and Applications
Sentic Computing: Techniques, Tools, and Applications
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The significant increase in content of online social media such as product reviews, blogs, forums etc., have led to an increasing attention to sentiment analysis tools and approaches that make use of mining this substantially growing content. The aim of this paper is to develop a robust classification approach of customer reviews based on a self-annotated domain-specific corpus by applying a statistical approach i.e., mutual information. First, subjective words in each test sentence are identified. Second, ambiguous adjectives such as high, low, large, many etc., are disambiguated based on their accompanying noun using a conditional mutual information approach. Third, a mutual information approach is applied to find the sentiment orientation (polarity) of the identified subjective words based on analyzing their statistical relationship with the manually annotated sentiment labels within a sizeable sentiment training data. Fourth, since negation plays a significant role in flipping the sentiment polarity of an identified sentiment word, we estimate the role of negation in affecting the classification accuracy. Finally, the identified polarity for each test sentence is evaluated against experts' annotation.